Image processing in fluorescence microscopy

Annotation: Fluorescence microscopy is one of the most important techniques used in current biomedical research. Analysis of images taken with a fluorescence microscope has its own specifics that need to be known to make this analysis accurate and effective. The candidate will become familiar with the principles of fluorescence microscopy and the main techniques and algorithms used in this area.

Continuous operators

Annotation: Contiguous operators are a class of operators in the field of mathematical morphology. Their main feature is that their application can not create new contours or the position of existing contours between regions can be changed. The candidate will learn about mathematical properties of contiguous operators, ways of their use for simplifying and segmenting the image and possible ways of their effective implementation.

Image segmentation using active curves

Annotation: Active curves resolve image segmentation as a minimization task. Basic models include geodetic active curves and active edgeless edges. The candidate will be familiarized with segmentation using these models and basic ways to find the minimum curve. Against Master's, emphasis is placed on a deeper understanding of the substance.

Image segmentation using graph theory

Annotation: The digital image can be represented by a graph where the nodes correspond to the pixels and the edges of the pixel relationships. Graph Theory offers many effective algorithms to solve optimization tasks (such as finding a minimal skeleton, shortest paths or minimal cuts). These algorithms are increasingly being applied to resolve the image segmentation problem. The candidate will learn basic segmentation models that are effectively solvable through graph theory.

Warp: segmentation by looking for the minimum skeleton, segmentation by looking for a minimum cut (Markov random fields), segmentation by searching for the shortest path, discussion of usability methods

Geometric Data Structures I

Annotation: Data structures and algorithms in the field of computer geometry are used to solve difficult problems in computer graphics. Based on these, algorithms have been developed in computer graphics that are effective in time and memory. A wide range of data structures and algorithms are creatively applied to address existing and new issues associated with processing and visualization of large data.

Geometric Data Structures II

Annotation: Data structures and algorithms in the field of computer geometry are used to solve difficult problems in computer graphics. Based on these, computer graphics have developed algorithms that are both time and memory efficient. A wide range of data structures and algorithms are creatively applied to address existing and new issues associated with processing and visualization of large data.

Monte Carlo methods in computer graphics

Annotation: Monte Carlo methods are used to solve problems from different areas, especially where the analytical solution of problems associated with, for example, multidimensional integrals is difficult or impossible. In computer graphics, they are especially used to solve global lighting tasks, in the form of random walks and sampling by importance.

Global lighting

Annotation: Algorithms for calculating global lighting are constantly evolving and enriching new physically-based light diffusion models in different environments. Their study is a prerequisite for the development of new effective methods of realistic scene rendering.

Volume Graphics

Annotation: Volume data, which come from different areas and are obtained by measurement, calculation, simulations, often contain important and interesting information that needs to be uncovered. The volume data graph deals with the segmentation and visualization of this information, which can be presented as solids and surfaces, or light-diffused spatial areas.

Numerical mathematics

Annotation: Numerical methods are key to solving many math problems on a computer. Understanding their principle and detailed knowledge of their limitations and accuracy are necessary for their proper use. The aim of the course is to enable the candidate to study numerical mathematics courses in their own specialization beyond the normal master courses. Emphasis will be placed on the theoretical mastering of the studied topic.

Warp: The subject of the exam will be the study of numerical mathematics topics beyond the usual master's courses (especially the courses PřF: M4180 and PřF: M5180) in agreement with the examiner. For example, linear / nonlinear systems of equations, numerical integration / derivation methods, function approximation and interpolation, methods of calculating own numbers and vectors, or numerical solution of differential equations can be mentioned as suitable topics.

Basic study material: It will be refined according to the chosen topic and focus of the student after the discussion with the examiner.

Tutor: prof. Ivana Horová, dr. Jiří Zelinka

Virtual Reality

Annotation: Virtual Reality (VR), sometimes referred to as immersive multimedia, is a computer-simulated environment that can simulate physical presence in real-world or imagined worlds. Virtual reality can recreate sensory experiences, which include virtual taste, sight, smell, sound, and touch. Virtual Reality technologies have advanced to the point that it is possible to develop and deploy meaningful, productive applications. The focus remains on the application of VR and the many issues that arise in application design and implementation, including hardware requirements, system integration, interaction techniques, and usability. This topic also counters both exaggerated claims for VR and the view that would reduce it to entertainment, quoting dozens of real-world examples from many different fields and presenting four in-depth application case studies.

Augmented Reality

Annotation:
Augmented Reality (AR), is a real-time technology that superimposes a digital information (3D objects, spatial sound, text, images and videos) directly onto a user’s view of the real environment.
The real world must be matched with the digital information in terms of the pose (position and orientation) to provide a meaningful augmentation.
Users can work either individually or collectively, experiment with virtual information and interact with the AR environment in a natural way. In an ideal AR scenario, the digital information
must be mixed with the real world in such a way that the user can either understand or not, the difference.